Dynamic retention policies and optional deletes

ABSTRACT

A method for execution by a computing device within a dispersed storage network (DSN). The method beings when data accesses occur for a data object of a storage container within the DSN. The method continues by updating, for at least some of the data accesses, an object value for the data object to produce an updated object value. The method continues by updating an object retention cost for the data object to produce an updated object retention cost. The method continues by updating a data object retention policy for the data object based on the updated object value and the updated object retentions costs. When one of the data accesses is a deletion event, the method continues by utilizing a current updated data object retention policy to determine a deletion-retention option for the data object. The method continues by executing the deletion-retention option on the data object.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a graph diagram of an embodiment of a dynamic retention policyin accordance with the present invention; and

FIG. 10 is a logic diagram of an example of a method of a dynamicretention policy in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generate aper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate a per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a graph diagram of an embodiment of a dynamic retentionpolicy. As illustrated in the graph, the x-axis represents retentioncosts and the y-axis represents object values. A retention cost for anobject is calculated based on one or more of a cost of access perobject, a cost per read, a cost per input-output process (TOP), storagecosts, lower quality of service (QoS) and a user subscription. An objectvalue is calculated based on one or more of a frequency of access, anobject type, a cost of access, company regulations, governmentregulations and overwrite occurrences.

The retention policy for a data object or container may be updated onevery access request, updated every certain number of access requests(e.g., every 50), updated according to a time schedule (e.g., once aminute, once an hour, etc.), or updated according to a user request. Asan example, for every non-delete access request for a data object, acomputing device applies inputs based on the request to a heuristicfunction, performs the heuristic function, updates a retention policybased on the results of the heuristic function, and stores the retentionpolicy within an object store along with the data object and containermetadata. Further, for every delete request, the computing device maycheck the retention policy to determine if the request is optional froman end user perspective. The retention policy may be checked by readingone or more of the object and container metadata.

The weighted line is a function that represents a dynamic retentionthreshold based on the storage system. The dynamic retention thresholdis calculated based on overall storage factors of the storage systemwhich include one or more of memory availability, memory utilization,reliability, data throughput rate and retention cost-value objectcomparisons (e.g., object 4 higher probability of delete than object 1).For example, when a system has a desired level of profitability, thethreshold may be adjusted (e.g., shifted up the y-axis, function changed(e.g., to an exponential function), etc.) so that the system tends tokeep more profitable objects in order to obtain the desired level ofprofitability. In one example, the system determines to keep objectsthat are above the dynamic retention threshold and invokes a retentionpolicy for objects that are below the dynamic retention threshold.

As a specific example, a computing device determines object 1 and object4 are below the dynamic retention threshold and determines objects 2 and3 are above the dynamic retention threshold. Having determined that dataobjects 1 and 4 are below the dynamic retention threshold, the computingdevice invokes the retention policy for objects 1 and 4. The computingdevice further determines that a container value is above the dynamicretention threshold and thus determines not to invoke the retentionpolicy for the container (e.g., keeps the container). Having determinedto invoke the retention policy for objects 1 and 4, the computing devicedetermines each data object's retention policy. For example, thecomputing device determines that object 1's retention policy is deletionand data object scrub without archiving and object 4's retention policyis deletion with archiving. Having determined the retention policies forobjects 1 and 4, the computing device then executes the retentionpolicies on objects 1 and 4. For example, the computing device deletesobjects 1 and 4 from the container, scrubs object 1 (e.g., overwritingdata object memory section with 0's one or more times, trimming theblocks of data corresponding to the data object, etc.), and archivesobject 4.

Other retention policies that may be invoked include a deletion witharchiving option, a deletion without archiving option, a deletion anddata object scrub without archiving option, a deletion prohibitionoption (e.g., government data, security policy, etc.), a deletionrestriction option based on one or more user parameters (e.g., userstatus, type of data, paid for service), and a subsequent data disposaloption. Note that each object's retention policy may be updated toinclude a different one of the data object retention polices.

FIG. 10 is a logic diagram of an example of a method of a dynamicretention policy in accordance with the present invention. The methodbegins at step 100, where a computing device of a dispersed storagenetwork (DSN) determines if a data access has occurred for a data objectof a storage container within the DSN. When the data access has notoccurred, the method loops back to step 100. When the data access hasoccurred, the method continues to step 102, where the computing devicedetermines if the data access is a deletion event.

When the data access is not a deletion event, the method continues tostep 104, where the computing device updates, for each of the dataaccesses, an object value for the data object to produce an updatedobject value. As an example, the computing device determines an updatedvalue factor based on one or more of frequency of the data accesses forthe data object, a type of the data object, a cost associated with oneor more of the data access, a regulation regarding deletion-retention ofthe data object, and overwriting occurrences (e.g., editing, revisions,etc.) of the data object. The computing device then changes a thencurrent object value based on the updated value factor to produce theupdated object value. As another example, the computing device updates,for each of the data accesses, a storage container value to produce anupdated storage container value (e.g., compilation of storage values forthe data objects in the container).

The method continues to step 106, where the computing device updates,for each of the data accesses, an object retention cost for the dataobject to produce an updated object retention cost. As an example, thecomputing device determines an updated cost factor based on one or moreof cost per access of the data object, cost per read of the data object,cost per input-output processing (TOP) of the data object, storage costsof the data object and user subscription criteria regarding the dataobject (e.g., quality of service (QoS), level of storage service,reliability (e.g., wider pillar number), etc.). Having produced theupdated cost factor, the computing device changes a then current objectretention cost based on the updated cost factor to produce the updatedretention cost. As another example, the computing device updates, foreach of the data accesses, a storage container cost to produce anupdated storage container cost (e.g., compilation of storage costs forthe data objects in the container).

The method continues to step 108, where the computing device updates,for each of the data accesses, a data object retention policy for thedata object based on the updated object value and the updated objectretentions costs. The data object retention policy includes one or moreof a deletion with archiving option, a deletion without archivingoption, a deletion and data object scrub without archiving option, adeletion prohibition option, a deletion restriction option based on oneor more user parameters (e.g., user status, type of data, paid forservice, etc.) and a subsequent data disposal option.

As an example of updating the data object retention policy, thecomputing device executes a heuristic function using the updated objectvalue and the updated object retention costs as inputs. Having executedthe heuristic function, the computing device then updates a then currentdata object retention policy based on a result of the heuristic functionto produce the updated data object retention policy. As another example,the computing device updates, for each of the data accesses, the dataobject retention policy for the data object based on the updated objectvalue, the updated object retentions costs, the updated storagecontainer value, and the updated storage container cost.

When the data access is a deletion event, the method continues to step110, where the computing device utilizes a current updated data objectretention policy to determine a deletion-retention option for the dataobject. As an example of the deletion event, the computing devicereceives a deletion request from a user device associated with the dataobject to produce the deletion event. As another example of the deletionevent, the deletion event is triggered by the DSN when a container, adata object or multiple data objects falls below a dynamic retentionthreshold. For example, the computing device determines a retentionthreshold for the data object based on the updated data object retentionpolicy, and when value-cost of the data object compares unfavorably tothe retention thresholding, initiates the deletion event.

The method continues to step 112, where the computing device executesthe deletion-retention option on the data object. As an example, thecomputing device receives a deletion request for a plurality of dataobjects stored in the storage container, wherein the plurality of dataobjects includes the data object. The computing device then, for eachdata object of the plurality of data objects, determines a then currentcorresponding data object retention policy. Having determined the thencurrent corresponding data object retention policy, the computing deviceexecutes the then current corresponding data object retention policy.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing devicewithin a dispersed storage network (DSN), the method comprises: whendata accesses occur for a data object of a storage container within theDSN; updating, for at least some of the data accesses, an object valuefor the data object to produce an updated object value; updating, for atleast some of the data accesses, an object retention cost for the dataobject to produce an updated object retention cost; and updating, for atleast some of the data accesses, a data object retention policy for thedata object based on the updated object value and the updated objectretention cost; when one of the data accesses is a deletion event,utilizing a current updated data object retention policy to determine adeletion-retention option for the data object; and executing thedeletion-retention option on the data object.
 2. The method of claim 1,wherein the updating the object value comprises: determining an updatedvalue factor based on one or more of: frequency of the data accesses forthe data object; a type of the data object; a cost associated with oneor more of the data accesses; a regulation regarding deletion-retentionof the data object; and overwriting occurrences of the data object; andchanging a then current object value based on the updated value factorto produce the updated object value.
 3. The method of claim 1, whereinthe updating the object retention cost comprises: determining an updatedcost factor based on one or more of: cost per access of the data object;cost per read of the data object; cost per input-output processing (IOP)of the data object; storage costs of the data object; and usersubscription criteria regarding the data object; and changing a thencurrent object retention cost based on the updated cost factor toproduce the updated object retention cost.
 4. The method of claim 1,wherein the updating the data object retention policy comprises:executing a heuristic function using the updated object value and theupdated object retention cost as inputs; and updating a then currentdata object retention policy based on a result of the heuristic functionto produce the updated data object retention policy.
 5. The method ofclaim 1 further comprises: receiving a deletion request from a userdevice associated with the data object to produce the deletion event. 6.The method of claim 1 further comprises: determining a retentionthreshold for the data object based on the updated data object retentionpolicy; and when value-cost of the data object compares unfavorably tothe retention threshold, initiating the deletion event.
 7. The method ofclaim 1 further comprises: receiving a deletion request for a pluralityof data objects stored in the storage container, wherein the pluralityof data objects includes the data object; and for each data object ofthe plurality of data objects: determining a then current correspondingdata object retention policy; and executing the then currentcorresponding data object retention policy.
 8. The method of claim 1further comprises: updating, for at least some of the data accesses,storage container value to produce an updated storage container value;updating, for at least some of the data accesses, a storage containercost to produce an updated storage container cost; and updating, for atleast some of the data accesses, the data object retention policy forthe data object based on the updated object value, the updated objectretention cost, the updated storage container value, and the updatedstorage container cost.
 9. The method of claim 1 further comprises: thedata object retention policy including one or more of: a deletion witharchiving option; a deletion without archiving option; a deletion anddata object scrub without archiving option; a deletion prohibitionoption; a deletion restriction option based on one or more userparameters; and a subsequent data disposal option; and the updated dataobject retention policy including a different one or more of: thedeletion with archiving option; the deletion without archiving option;the deletion and data object scrub without archiving option; thedeletion prohibition option; the deletion restriction option based onone or more user parameters; and the subsequent data disposal option.10. A computing device comprises: memory; an interface; and a processingmodule operably coupled to the interface and memory, wherein theprocessing module is operable to: when data accesses occur for a dataobject of a storage container within a dispersed storage network (DSN);update, for at least some of the data accesses, an object value for thedata object to produce an updated object value; update, for at leastsome of the data accesses, an object retention cost for the data objectto produce an updated object retention cost; and update, for at leastsome of the data accesses, a data object retention policy for the dataobject based on the updated object value and the updated objectretentions costs; when one of the data accesses is a deletion event,utilize a current updated data object retention policy to determine adeletion-retention option for the data object; and execute thedeletion-retention option on the data object.
 11. The computing deviceof claim 10, wherein the processing module is further operable to updatethe object value by: determining an updated value factor based on one ormore of: frequency of the data accesses for the data object; a type ofthe data object; a cost associated with one or more of the dataaccesses; a regulation regarding deletion-retention of the data object;and overwriting occurrences of the data object; and changing a thencurrent object value based on the updated value factor to produce theupdated object value.
 12. The computing device of claim 10, wherein theprocessing module is operable to update the object retention cost by:determining an updated cost factor based on one or more of: cost peraccess of the data object; cost per read of the data object; cost perinput-output processing (TOP) of the data object; storage costs of thedata object; and user subscription criteria regarding the data object;and changing a then current object retention cost based on the updatedcost factor to produce the updated object retention cost.
 13. Thecomputing device of claim 10, wherein the processing module is furtheroperable to update the data object retention policy by: executing aheuristic function using the updated object value and the updated objectretention cost as inputs; and updating a then current data objectretention policy based on a result of the heuristic function to producethe updated data object retention policy.
 14. The computing device ofclaim 10, wherein the processing module is further operable to: receivea deletion request from a user device associated with the data object toproduce the deletion event.
 15. The computing device of claim 10,wherein the processing module is further operable to: determine aretention threshold for the data object based on the updated data objectretention policy; and when value-cost of the data object comparesunfavorably to the retention threshold, initiate the deletion event. 16.The computing device of claim 10, wherein the processing module isfurther operable to: receive a deletion request for a plurality of dataobjects stored in the storage container, wherein the plurality of dataobjects includes the data object; and for each data object of theplurality of data objects: determine a then current corresponding dataobject retention policy; and execute the then current corresponding dataobject retention policy.
 17. The computing device of claim 10, whereinthe processing module is further operable to: update, for at least someof the data accesses, storage container value to produce an updatedstorage container value; update, for at least some of the data accesses,a storage container cost to produce an updated storage container cost;and update, for at least some of the data accesses, the data objectretention policy for the data object based on the updated object value,the updated object retentions costs, the updated storage containervalue, and the updated storage container cost.
 18. The computing deviceof claim 10 further comprises: the data object retention policyincluding one or more of: a deletion with archiving option; a deletionwithout archiving option; a deletion and data object scrub withoutarchiving option; a deletion prohibition option; a deletion restrictionoption based on one or more user parameters; and a subsequent datadisposal option; and the updated data object retention policy includinga different one or more of: the deletion with archiving option; thedeletion without archiving option; the deletion and data object scrubwithout archiving option; the deletion prohibition option; the deletionrestriction option based on one or more user parameters; and thesubsequent data disposal option.